Granulometries and Opening Trees

Granulometries constitute one of the most useful and versatile sets of tools of morphological image analysis. They can be applied to a wide range of tasks, such as feature extraction, texture characterization, size estimation, image segmentation, etc., both for binary and for grayscale images. However, for most applications, traditional granulometry algorithms - involving sequences of openings or closings with structuring elements of increasing size - are prohibitively costly on non-specialized hardware. This has prevented granulometries from reaching a high level of popularity in the image analysis community. This paper addresses the computational aspect of granulometries and proposes a comprehensive set of fast algorithms. In binary images, all but the simplest cases (namely linear granulometries based on openings with line segments) require the prior extraction of opening transforms (also referred to as “granulometry functions”). A very efficient algorithm is proposed for the computation of the most useful opening transforms. In grayscale images, linear granulometries are considered first and a particularly efficient algorithm is described. The concept of an opening tree is then proposed as a gray extension of the opening transform. It forms the basis of a novel technique for computing granulometries based on maxima of openings by line segments in different orientations, as well as pseudo-granulometries based on minima of linear openings. Furthermore, opening trees can be used in local granulometry algorithms, thereby making it possible to compute such objects as size transforms directly from grayscale images. Other applications include adaptive openings and closings, as well as granulometric texture segmentation. The efficiency of this set of algorithms greatly increases the range of problems that can be addressed using granulometries. A number of applications are used throughout the paper to illustrate the usefulness of the proposed techniques.

[1]  Philippe Salembier,et al.  Flat zones filtering, connected operators, and filters by reconstruction , 1995, IEEE Trans. Image Process..

[2]  Gunilla Borgefors,et al.  Distance transformations in digital images , 1986, Comput. Vis. Graph. Image Process..

[3]  P. Delfiner A generalization of the concept of size , 1972 .

[4]  Luc M. Vincent New trends in morphological algorithms , 1991, Electronic Imaging.

[5]  A. ROSENFELD,et al.  Distance functions on digital pictures , 1968, Pattern Recognit..

[6]  G. Matheron Random Sets and Integral Geometry , 1976 .

[7]  Jordi Vitrià,et al.  Texture Classification Using Neural Networks and Local Granulometries , 1994, ISMM.

[8]  Edward R. Dougherty,et al.  Gray-scale granulometries compatible with spatial scalings , 1993, Signal Process..

[9]  Petros Maragos,et al.  Morphological systems for character image processing and recognition , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[10]  Azriel Rosenfeld,et al.  Sequential Operations in Digital Picture Processing , 1966, JACM.

[11]  Luc M. Vincent,et al.  Efficient computation of various types of skeletons , 1991, Medical Imaging.

[12]  Luc Vincent,et al.  Local Grayscale Granulometries Based on Opening Trees , 1996, ISMM.

[13]  Michel Schmitt Variations on a theme in binary mathematical morphology , 1991, J. Vis. Commun. Image Represent..

[14]  Linda G. Shapiro,et al.  Computer and Robot Vision , 1991 .

[15]  Jean Serra,et al.  Image Analysis and Mathematical Morphology , 1983 .

[16]  Luc Vincent,et al.  Morphological transformations of binary images with arbitrary structuring elements , 1991, Signal Process..

[17]  Robert M. Haralick,et al.  Recursive opening transform , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[18]  Jeff B. Pelz,et al.  Morphological image segmentation by local granulometric size distributions , 1992, J. Electronic Imaging.

[19]  Gilles Bertrand,et al.  An algorithm for a generalized distance transformation based on Minkowski operations , 1988, [1988 Proceedings] 9th International Conference on Pattern Recognition.

[20]  G. Matheron Éléments pour une théorie des milieux poreux , 1967 .

[21]  L. Vincent Grayscale area openings and closings, their efficient implementation and applications , 1993 .

[22]  Maria Vanrell,et al.  Mathematical morphology, granulometries, and texture perception , 1993, Optics & Photonics.

[23]  A. Solow,et al.  Microaggregations of Oceanic Plankton Observed by Towed Video Microscopy , 1992, Science.

[24]  Petros Maragos,et al.  Pattern Spectrum and Multiscale Shape Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[25]  Edward R. Dougherty,et al.  Texture classification by gray-scale morphological granulometries , 1992, Other Conferences.

[26]  Luc Vincent Fast Grayscale Granulometry Algorithms , 1994, ISMM.

[27]  M. Schmitt,et al.  Shape recognition combining mathematical morphology and neural networks , 1991 .